School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei 430070, China.
Research Center of Nonlinear Science, Wuhan Textile University, Wuhan, Hubei 430070, China.
Chaos. 2021 Jan;31(1):013128. doi: 10.1063/5.0036933.
Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [P(k)] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database.
纹理分类在图像分析和其他一些相关领域中得到了广泛的应用。在本文中，我们基于 Lacasa 等人提出的一种新的图像可视性图网络构建方法，设计了一种纹理分类算法，命名为 TCIVG（基于图像可视性图的纹理分类）。通过在 Brodatz 纹理图像数据库上使用 TCIVG，我们展示了整个过程。首先，将图像数据库中的每个纹理图像转换为关联的图像自然可视性图网络和图像水平可视性图网络。然后，提取度分布度量[P(k)]作为不同分类器的关键特征参数。数值实验表明，对于人工纹理图像，可以通过基于自然 TCIVG 的二次判别获得 100%的分类精度。对于自然纹理图像，可以通过基于水平 TCIVG 的线性 SVM（支持向量机）获得 94.80%的分类精度。我们的结果优于基于相同图像数据库的一些现有文献研究的报告结果。
